This repository contains a similar model as what is called supervised model in following paper:

• Rohrbach, Anna, et al. "Grounding of textual phrases in images by reconstruction." arXiv preprint arXiv:1511.03745 (2015).*

The preprocessing part is borrowed from link and link

THe difference to the original paper is: I use logistic loss instead of softmax; and I use bi-directional LSTM; adn I didn't use batch normalization.

The best result I can get on test is around 31, which is the state-of-art.

I uses torch to train, and caffe to do preprocessing(copied from link and link)

Torch packages requirement: dpnn. rnn, cbp, torchx, nngraph

Caffe installation:

1. Run ./external/download_caffe.sh to download the SCRC Caffe version for this experiment. It will be downloaded and unzipped into external/caffe-natural-language-object-retrieval. This version is modified from the Caffe LRCN implementation.
2. Build the SCRC Caffe version in external/caffe-natural-language-object-retrieval, following the Caffe installation instruction. Remember to also build pycaffe.

1. Download the ReferIt dataset: ./datasets/download_referit_dataset.sh.
2. Download pre-extracted EdgeBox proposals: ./data/download_edgebox_proposals.sh.
3. Preprocess the ReferIt dataset to generate metadata needed for training and evaluation: python ./exp-referit/preprocess_dataset.py.
4. Cache the scene-level contextual features to disk: python ./exp-referit/cache_referit_context_features.py.
5. Cache the local-level features for ground truth regions to disk: python ./exp-referit/cache_referit_local_features.py.
6. Cache the local-level features for edgebox regions to disk: python ./exp-referit/cache_edgebox_feature.py.
7. python my_cache_batches.py --dataset train/val/test to get train/val/test pairs

To train the model, run

th train.lua -visnet_type rt -loss_type logistic -learning_rate 1e-4 -save_checkpoint_every 40000 -val_images_use 15000 -load_best_score 0

To eval the model, run

th eval.lua -checkpoint_start_from model_id.t7 -loss_type logistic

If you want to initialize with word2vec: You should first run python load_w2v.py

Then during training, add option -word2vec 1.